In [1]:
import numpy as np
import pandas as pd 
import matplotlib.pyplot as plt
import seaborn as sns
In [2]:
car = pd.read_csv('../data/cleaned_car_price.csv')
In [3]:
car[car['Manufacturer'] == 'DODGE']
Out[3]:
ID Price Levy Manufacturer Model Prod. year Category Leather interior Fuel type Engine volume Mileage Cylinders Gear box type Drive wheels Doors Wheel Color Airbags Turbo
543 45791030 25089 896 DODGE Challenger 2012 Coupe No Petrol 3.6 112000 6.0 Tiptronic Rear 2 Left wheel Grey 6 0
638 45806621 1882 1399 DODGE Journey 2010 Jeep Yes Petrol 3.5 243477 6.0 Automatic Front 4 Left wheel Blue 12 0
750 44677324 16308 896 DODGE Dart Limited 2013 Sedan Yes Petrol 1.4 180000 4.0 Manual Front 4 Left wheel Black 12 1
910 45797949 18189 900 DODGE Dart GT 2.4 2015 Sedan Yes Petrol 2.4 48000 4.0 Automatic Front 4 Left wheel White 4 0
915 45813829 3058 1083 DODGE Challenger 2013 Coupe Yes Petrol 3.6 232853 6.0 Automatic Rear 2 Left wheel Black 12 0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
15536 45771137 11604 1760 DODGE Challenger 2017 Sedan Yes Petrol 3.6 240680 6.0 Automatic Rear 4 Left wheel Grey 12 0
16278 45770567 8154 1095 DODGE Caliber 2008 Hatchback No Petrol 2.0 154000 4.0 Automatic Front 4 Left wheel White 4 0
16736 45755529 1646 707 DODGE Caliber 2011 Hatchback Yes Petrol 2.0 168166 4.0 Automatic Front 4 Left wheel Orange 12 0
16744 45771556 9565 2377 DODGE Ramcharger 2016 Sedan Yes Petrol 5.7 39013 8.0 Automatic Rear 4 Left wheel Black 12 0
17160 44571028 8781 896 DODGE Caliber 2008 Hatchback No Petrol 2.4 0 4.0 Automatic Front 4 Left wheel Blue 4 0

85 rows × 19 columns

In [4]:
car['Model'].value_counts()
Out[4]:
Sonata          987
Prius           900
Elantra         871
Camry           784
Santa FE        517
               ... 
FIT RS            1
500 s             1
325 CI            1
Move              1
Prius C aqua      1
Name: Model, Length: 1530, dtype: int64

Exploratory Data Analysis¶

In [5]:
fig, axs = plt.subplots(nrows = 3, ncols = 2, figsize = (16,16))
columns = ['Category', 'Fuel type', 'Cylinders', 'Gear box type', 'Airbags', 'Color']
colors = ['r', 'g', 'b', 'y', 'm', 'c']

for i, column in enumerate(columns):
    row = i // 2
    col = i % 2
    car[column].value_counts().plot(kind = 'bar', ax = axs[row,col], color = colors[i % len(colors)])
    axs[row,col].set_title(column)
    
plt.tight_layout()
plt.show()
In [6]:
fig, axs = plt.subplots(nrows = 2, ncols = 2, figsize = (12,8))
columns = ['Leather interior', 'Wheel', 'Doors', 'Turbo']
colors = [['r', 'g', 'b'],[ 'y', 'm', 'c'],['y', 'b', 'r'],['b', 'pink', 'g']]

for i, column in enumerate(columns):
    row = i // 2
    col = i % 2
    car[column].value_counts().plot(kind = 'pie', ax = axs[row,col], colors = colors[i])
    axs[row,col].set_title(column)
    
plt.tight_layout()
plt.show()
In [7]:
fig, axs = plt.subplots(nrows = 3, figsize = (12,8))
plt.subplots_adjust(hspace = 0.6)
car['Prod. year'].value_counts().plot(kind = 'bar', color = 'c', ax = axs[0])
car['Manufacturer'].value_counts().plot(kind = 'bar', color = 'g', ax = axs[1])
car['Engine volume'].value_counts().plot(kind = 'bar', color = 'r', ax = axs[2])
Out[7]:
<AxesSubplot:>
In [8]:
def distPlot(column,r,c,colr):
    sns.distplot(car[column], ax = axs[r,c], color = colr)

def boxPlot(column,r,c,colr):
    sns.boxplot(car[column], ax = axs[r,c], color = colr)
    
def histPlot(column,r,c,colr):
    sns.histplot(np.log(car[column]), ax = axs[r,c], color = colr)
In [9]:
fig, axs = plt.subplots(nrows = 4, ncols = 3, figsize = (20,20))
columns = ['Price','Mileage','Levy','Prod. year']

for i,column in enumerate(columns):
    distPlot(column,i,0,'blue')
    boxPlot(column,i,1,'grey')
    histPlot(column,i,2,'red')
    axs[i,2].set_xlabel('Log of {}'.format(column))
    
plt.tight_layout()
plt.show()

C:\Users\Golden\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
C:\Users\Golden\anaconda3\lib\site-packages\seaborn\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(
C:\Users\Golden\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
C:\Users\Golden\anaconda3\lib\site-packages\seaborn\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(
C:\Users\Golden\anaconda3\lib\site-packages\pandas\core\arraylike.py:397: RuntimeWarning: divide by zero encountered in log
  result = getattr(ufunc, method)(*inputs, **kwargs)
C:\Users\Golden\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
C:\Users\Golden\anaconda3\lib\site-packages\seaborn\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(
C:\Users\Golden\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
C:\Users\Golden\anaconda3\lib\site-packages\seaborn\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(
In [10]:
def scattPlot(column,r,c,colr):
    sns.scatterplot(x = car[column], y = car['Price'], color = colr, ax = axs[r,c])
    
def kdePlot(column,r,c,colr):
    sns.kdeplot(x = car[column], y = car['Price'], shade = True, cmap = colr, ax = axs[r,c])
In [11]:
fig, axs = plt.subplots(nrows = 4, ncols = 2, figsize = (20,20))
columns = ['Mileage','Levy','Prod. year','Engine volume']
colors = ['purple', 'red', 'blue', 'orange']
colorsSeq = ['Purples_r', 'Reds_r', 'Blues_r', 'Oranges_r']

for i, column in enumerate(columns):
    scattPlot(column,i,0,colors[i])
    kdePlot(column,i,1,colorsSeq[i])
    
plt.show()
In [12]:
import plotly.express as px
production = car[['Manufacturer', 'Category', 'Cylinders']]
fig = px.sunburst(production, path = ['Manufacturer', 'Category'],
                 values = 'Cylinders', width = 950, height = 1000,
                 color = 'Cylinders', color_continuous_scale = 'IceFire',
                 title = "Cars Production with Cylinder Counts")

fig.show()
In [13]:
fig, axs = plt.subplots(nrows = 4, ncols = 3, figsize = (20,20))
fig.subplots_adjust(hspace = 0.3)
axs = axs.flatten() 
columns = ['Manufacturer', 'Category', 'Leather interior', 'Fuel type',
           'Cylinders', 'Gear box type', 'Drive wheels', 'Doors', 'Wheel',
           'Color', 'Airbags', 'Turbo']
colors = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b',
          '#e377c2', '#7f7f7f', '#bcbd22', '#17becf', 'blue', 'red']

for i, column in enumerate(columns):
    sns.barplot(x = column, y = 'Price', data = car, ax = axs[i], palette = [colors[i]])
    axs[i].set_title(f'Price vs {column}')
    axs[i].set_xticklabels(axs[i].get_xticks(), rotation = 90)
    axs[i].set_xlabel('')

plt.show()
In [14]:
plt.figure(figsize=(12,8))
axs = sns.barplot(x = 'Manufacturer', y = 'Price', data = car, palette = 'inferno')
axs.set_xticklabels(axs.get_xticklabels(), rotation = 90)
plt.title('Price by Manufacturer')
plt.show()